Particulate Matter Concentration Levels Research Articles

Air Quality Monitoring with Low-Cost Sensors: A Record of the Increase of PM2.5 during Christmas and New Year’s Eve Celebrations in the City of Queretaro, Mexico

The high concentrations of particulate matter (PM) in the atmosphere have adverse effects on both the environment and human health, as well as on urban and faunal biodiversity. Short-term events, such as the burning of fireworks, attributable to human activity, result in a significant and rapid increase in PM concentration levels within the atmosphere. We present findings derived from observations made using low-cost PM sensors deployed as part of the Environmental Monitoring Network in Querétaro, Mexico. These sensors utilize the Environmental Geo Monitoring Stations (EMGA) device, developed by the Institute of Geosciences at UNAM. The observation period spanned from 10 December 2023 to 10 January 2024, encompassed the Christmas and New Year holidays, and focused on PM2.5 concentrations, and a comparison with a similar period from 10 February 2024 to 10 March 2024 considered a typical activity period in the area. The results reveal two substantial increases in PM2.5 concentrations (from less than 50 µg/m3 to more than 340 µg/m3) over time and spatial distribution during this period, with notable increases observed during the festive season coincident with the Christmas and New Year celebrations. Specifically, prolonged periods exceeding up to 6 h were noted on festivity days. This research offers insight into the effects, trends, and spatial–temporal distribution of pollutants within the city of Queretaro, which may be used as a reference for other cities around the world.

Assessment of the Ambient Air Quality of Sangli City, Maharashtra, vis-à-vis National Ambient Air Quality Standards (NAAQS)

Assessment of ambient air quality is important to formulate short-term and long-term plans to mitigate air pollution. This study attempts to present an ambient air quality assessment for Sangli City, located at 16.85° N, and 74.58° E. The assessment is done through ambient air quality data measured over three ambient air quality monitoring stations located in residential, commercial, and industrial zones of Sangli City. The data was collected from 2009 to 2017 under the National Air Quality Monitoring Program of the Central Pollution Control Board (CPCB), India. The observed concentrations of gaseous pollutants, SO2 and NOx, and respirable particulate matter are compared with the concentration levels prescribed by the National Ambient Air Quality Standards (NAAQS) in 2009. The concentration levels of particulate matter are quite on the higher side of the prescribed standards. The levels of NOx are almost approaching the prescribed values, whereas SO2 levels are well within the margin. We have also studied seasonal variations in the pollutants during the study period. In general, the air quality in Sangli City is quite undesirable during the mid-winter to mid-summer period. The study is useful for common men to increase awareness. It also forms baseline data and is useful for local and central government to design appropriate strategies to apply corrective measures, protect health, and promote environmental protection.

Composition analysis and health risk assessment of the hazardous compounds in cooking fumes emitted from heated soybean oils with different refining levels

The cooking fumes generated from thermal cooking oils contains various of hazardous components and shows deleterious health effects. The edible oil refining is designed to improve the oil quality and safety. While, there remains unknown about the connections between the characteristics and health risks of the cooking fumes and oils with different refining levels. In this study, the hazardous compounds, including aldehydes, ketones, polycyclic aromatic hydrocarbons (PAHs), and particulate matter (PM) in the fumes emitted from heated soybean oils with different refining levels were characterized, and their health risks were assessed. Results demonstrated that the concentration range of aldehydes and ketones (from 328.06 ± 24.64 to 796.52 ± 29.67 μg/m3), PAHs (from 4.39 ± 0.19 to 7.86 ± 0.51 μg/m3), and PM (from 0.36 ± 0.14 to 5.08 ± 0.15 mg/m3) varied among soybean oil with different refining levels, respectively. The neutralized oil showed the highest concentration of aldehydes and ketones, whereas the refined oil showed the lowest. The highest concentration levels of PAHs and PM were observed in fumes emitted from crude oil. A highly significant (p < 0.001) positive correlation between the acid value of cooking oil and the concentrations of PM was found, suggesting that removing free fatty acids is critical for mitigating PM concentration in cooking fumes. Additionally, the incremental lifetime cancer risk (ILCR) values of PAHs and aldehydes were 5.60 × 10−4 to 8.66 × 10−5 and 5.60 × 10−4 to 8.66 × 10−5, respectively, which were substantially higher than the acceptable levels (1.0 × 10−6) established by US EPA. The present study quantifies the impact of edible oil refining on hazardous compound emissions and provides a theoretical basis for controlling the health risks of cooking fumes via precise edible oil processing.

Soft sensor for predicting indoor PM2.5 concentration in subway with adaptive boosting deep learning model

Public health depends on indoor air quality (IAQ), hence soft measurement techniques must be implemented in the subway environment for more precise and reliable monitoring of indoor particulate matter concentration levels. Adaptive boosting (AdaBoost), an ensemble learning technique, is simple to code and less prone to overfitting. Compared to a single model, it is better able to take into consideration the intricate elements included in air quality data. It is suggested to use an adaptive boosting of long short-term memory (AdaBoost-LSTM) model and kernel principal component analysis (KPCA) for ensemble learning. The kernel function and PCA are first coupled to create KPCA, which is a nonlinear dimensionality reduction method for IAQ. This removes the negative impacts of noise interference. The learning performance of LSTM is then enhanced using AdaBoost as an ensemble learning technique. The KPCA-AdaBoost-LSTM model can deliver higher modeling performance, according to the results. The R2 reached 0.9007 and 0.8995 when predicting PM2.5 in the hall and platform. SHapley Additive exPlanations (SHAP) analysis was used to interpret the input contributions of the model, enhancing the interpretability and transparency of the proposed soft sensor.

Environmental impact of quarrying on air quality in Ebonyi State, Nigeria

AbstractThe insatiable demand for rock supplies has enticed numerous building and construction enterprises to participate in stone quarrying. This paper examines the environmental impact of quarrying on air quality in Ebonyi State, Nigeria. To achieve the main aim of the study, an objective was set to detect air pollutants at the quarry sites. A total of 220 air samples were measured from six points around the quarry locations and recorded in situ for analysis. The samples were measured three times a day (morning, afternoon, and evening) for three days. Gas monitors were used to monitor air pollutants. The generated data were subjected to completely random design (CRD) sampling techniques. The separation of means and tests was performed using Fisher's Least Significant Difference (FLSD) at a significance probability level of 5%. Based on statistical analysis, the findings detected significantly higher concentration levels of particulate matter, nitrogen dioxide, hydrogen sulfide, carbon monoxide, sulfur dioxide, chlorine, volatile organic compounds, ammonia, and hydrogen cyanide in the quarry areas than the value detected in the control area. The findings also confirmed higher noise levels in the locations. It was also observed that the concentration levels of the parameters differed from point to point and at different times of the day. This really means the occurrence of a high rate of air pollution in the study locations. Based on the above findings, it is highly recommended that air pollution control equipment be installed at quarry sites in order to reduce gaseous (pollutant) emissions.

Environmental impact of quarrying on air quality in Ebonyi state, Nigeria

BackgroundThe insatiable demand for rock supplies has enticed numerous building and construction enterprises to participate in stone quarrying. However, this has had an environmental impact on air quality. This paper examines the environmental impact of quarrying on air quality in Ebonyi State, Nigeria. To achieve the main aim of the study, an objective was set to detect air pollutants at the quarry sites. A total of 220 air samples were measured from six points around the quarry locations and recorded in situ for analysis. The samples were measured three times a day (morning, afternoon, and evening) for three days. Gas monitors were used to monitor air pollutants. The generated data were subjected to completely random design (CRD) sampling techniques. The separation of means and tests was performed using Fisher’s Least Significant Difference (FLSD) at a significance probability level of 5%.ResultsBased on statistical analysis, the findings detected significantly higher concentration levels of particulate matter, nitrogen dioxide, hydrogen sulphide, carbon monoxide, sulphur dioxide, chlorine, volatile organic compounds, ammonia, and hydrogen cyanide in the quarry areas than the value detected in the control area. The findings also confirmed higher noise levels in the locations. It was also observed that the concentration levels of the parameters differed from point to point and at different times of the day. This really means the occurrence of a high rate of air pollution in the study locations.ConclusionsBased on the above findings, it is highly recommended that, (i) if situation is not timely addressed, it will lead to a severe environmental disaster or hazard, as nobody selects the air he or she breathes; (ii) air pollution control equipment be installed in-situ at quarry sites where free air flow is available in order to reduce gaseous (pollutant) emissions, and (iii) the seasonal effects, meteorological parameters and time that were influenced by the activities of quarry should be put in check.

미세먼지의 야외 여가활동 수요에 대한 효과

As the damage of particulate matter (PM) becomes known to the public recently, both mitigation and adaptation to highly concentrated PM have been emphasized. While it has been found that the tendency to avoid PM can also affect the decision-making of outdoor activities, some studies show a positive correlation between PM concentration and visitations. In this extension, this study examines how PM concentration affects the visiting behavior of national parks. For this, we used the actual daily visitation data (from January 1, 2014 to June 30, 2020) to 20 national parks from Korea National Park Service by adopting the fixed effect panel model. The results showed that a positive relationship between the number of visitors and PM concentration but it is insignificant. The results also showed that people are more sensitive to whether or not the high PM concentration than to PM concentration value itself and they reduce visitations as PM concentration level becomes ‘bad’ or worse. In addition, the correlation between PM concentration and visitations appears in a different pattern in winter than in other seasons. These results implies that a simple analysis on the relationship between PM concentration and outdoor activity can lead to misunderstanding and that there is something else people consider more than PM concentration level.

Design and Implementation of a Particulate Matter Measurement System for Energy-Efficient Searching of Air Pollution Sources Using a Multirotor Robot

Analyzing air pollutants is of key importance for the environmental protection goals. High concentrations of particulate matter (PM) have a particularly negative impact on human life and health. The use of an autonomous multirotor flying robot (drone) for the purposes of locating PM sources requires the design of a dedicated measurement system from scratch. The aim of this study was to make the most important design decision, which is the correct localization of the inlet of the measurement system, taking into account disturbances in the flow field caused by the rotors. To achieve this, a computational model was built with the use of a finite-volume method in Ansys Fluent software. Based on its results, a novel criterion was proposed and applied. In addition to the trivial position outside the rotors on the extended arm, it gave the second location in the space limited by the rotors below the robot. Finally, a robot prototype was built, and a series of verification experiments were carried out, first indoors and then outdoors, at different levels of ambient PM concentrations with and without a pollution source. The field results were compiled as histograms and scatter plots and clearly demonstrated the validity of the adopted criterion. The determination coefficient between measured concentrations showed a stronger relationship when the rotors were operating. Furthermore, in cases with a present pollution source, higher concentrations were observed around the internal sensor, making it more suitable for the studied application.

Assessing particulate matter concentration level and its limit exceedance based on year-round field measurements of different laying hen building systems

Assessing particulate matter concentration level and its limit exceedance based on year-round field measurements of different laying hen building systems

Environmental Risk Assessment from 2018 To 2022 for Kota, Rajasthan (India)

Particulate matter pollution in the metropolis has become an international concern because of its dangerous short and long-term effects on humans and the environment. This research aims to quantify particulate matter's severe impact on inhabitants and identify the ecological environment risk category of Kota city, Rajasthan (India), throughout the selected study period from 2018 to 2022 for four years. Human health risk assessment has been assessed through AirQ+ software (WHO invented), while ecological hazard risk categories were recognised through risk quotient (RQ). The present scenario of particulate matter concentration is compared with standards given by different regulating agencies (WHO, USEPA, and Indian NAAQS) to verify particulate matter pollution. The current particulate matter concentration levels of Kota city are also compared with different regional cities of Rajasthan (India), namely, Jaipur, Udaipur, Ajmer, Pali, Alwar, and Jodhpur. The dust ratio (PM2.5/PM10) is computed for Kota and regional cities to validate the increasing levels of fine particulates than the larger ones. The four-year average concentration of PM10 and PM2.5 were 121 and 58 µg/m3, respectively, with a dust ratio of 0.48. Particulate matter concentrations (PM10 and PM2.5) are violating the standards set by environmental agencies during the study period. The mean risk quotient (RQ) is 2.02 for PM10 and 1.43 for PM2.5, which implies a high-risk hazard category (RQ &gt; 1) in the ecological environment of Kota city. The mortality cases evaluated from AirQ+ software were 5024 for all natural causes, 885 for lung cancer, 272 for acute lower respiratory infection, 464 for COPD, 2060 for IHD, and 1880 for stroke. The number of hospital admissions was 1485 for respiratory disease, 58 for cardiovascular disease, and 784 for adult mortality (30+ years) to PM2.5. Chronic bronchitis incidence in adults was 14469, postneonatal infant mortality was 355816, and the prevalence of bronchitis in children was 767 due to PM10 exposure for a long time, while asthma symptoms in asthmatic children were 349 due to exposure for a short time. The results of this study are terrifying, and it is an earlier sign to government representatives and stakeholders to implement the new policies and technologies to curb the pollution level originating from particulate matter; otherwise, impacts on the environment become more acute.

Parameters influencing population annoyance pertaining to air pollution

Annoyance caused by particulate matter qualifies as a public health problem, as it can be seen as an ambient stressor causing stress, diseases and affecting the quality of life. Previous studies have shown a complex relationship between annoyance and perception of air pollution, health problems occurrences, air pollutants concentration levels and location within the urban area, which should be better investigated. The objective of this work is to identify the parameters that influences perceived annoyance caused by air pollution. Additionally, we propose the analyses of exposure-response relationship between perceived annoyance and concentration levels of particulate matter (TSP and PM10). Questionnaire-based surveys were conducted in sub regions around of the air quality monitoring stations in metropolitan area of Vitoria, Brazil. Logistic regression was used to identify the associations between qualitative questionnaire variables and air pollution expressed by PM concentrations. Results have shown that 90% of the population reports nuisance by air pollution and about 80% of respondents frequently perceived air pollution by dust. The determinants parameters and subjective factors related to particles annoyance perception can be used in order to predict air pollution impact on the studied region population. By exposure-response relationship when the concentration level of PM increases, the probability of being annoyed also grows. The results and the methodology discussed here can be very useful for planning purposes where the stakeholders usually do not have access to detailed information, especially for micro-management in a regional or city-planning level.

Ambient air pollutant concentrations and asthma-related hospital admissions during COVID-19 transport restrictions

ObjectivesExposure to air pollution is a known risk factor for asthma exacerbations and hospitalisations. This study aimed to identify if COVID-19 transport restrictions led to improvements in air quality in Dublin and if this had an impact on asthma-related hospital admissions. Study designThis was a population-based retrospective cohort study. MethodsDaily concentration levels of particulate matter (PM2.5 and PM10) and nitrogen dioxide (NO2) were obtained from the Environmental Protection Agency (EPA). The Hospital In-Patient Enquiry (HIPE) system provided the daily number of asthma-related hospital admissions in Dublin. The figures for 2018–2019 were compared with the period of transport restrictions (from March 2020). ResultsDuring the period of transport restrictions, there was a significant decrease in mean daily concentrations in both PM2.5 (8.9 vs 7.8 μg/m3, P = 0.002) and NO2 (24.0 vs 16.7 μg/m3, P < 0.001). There was also a significant reduction in the mean number of daily asthma admissions (4.5 vs 2.8 admissions, P < 0.001). Only NO2 showed a statistically significant correlation with asthma admissions (r = 0.132, P < 0.001). ConclusionTransport restrictions introduced to mitigate against COVID-19 led to lower pollutant levels and improved air quality. Previously described associations between pollutants and asthma would indicate that these improvements in air quality contributed to the reduction in asthma-related admissions. The complex nature of PM is the likely explanation for the lack of correlation between its concentration and asthma admissions, unlike NO2 whose primary source is vehicular emissions. Public Health needs to advocate for transport policies, which can improve air quality and hence improve human health.

Long-Range Air Pollution Monitoring

Air pollution in major cities worldwide today has become an important topic due to its adverse effects on human health and the environment. This research aims to monitor particulate matter (PM) at different sizes, i.e., less than 1 μm (PM1), 2.5 μm (PM2.5), 4 μm (PM4), and 10 μm (PM10), and that the concentration of particulate matter changes with location and time. Also, particulate matter is one of the primary pollutants in the air, which affects the environment and the risk of human mortality and morbidity of respiratory disease. This research presents the design and development of a low-cost network using LoRa (short for long-range), a spread spectrum modulation technique derived from Chirp Spread Spectrum (CSS) technology. Semtech’s LoRa is a long-range, low power wireless platform that has become the de-facto wireless platform of the Internet of Things (IoT). For detecting particulate matter levels, a commercially available Sensirion sensor (SPS30) was purchased and used. The developed and the deployed network has these sensors connected to LoRa modules (senor nodes) with an ESP32 microcontroller programmed to collect and send data to a gateway using the 915 MHz frequency band. The gateway then sends the data to ‘The Things Network (TTN)’, where a developed cloud-based dashboard reads the data. Several sensor nodes collect the measured values in the air at different elevations at the monitoring location. The proposed network design has been implemented at a specified location in Auckland City Centre, New Zealand. The designed network system allows the users to access a developed online dashboard, which shows the different concentration levels of particulate matter in the air in real-time.

TD-GC/MS analysis of indoor air pollutants (VOCs, PM) in hair salons

Hairdressing personnel is daily exposed to various chemical air pollutants, and specifically to Volatile Organic Compounds (VOCs) and Particulate Matter (PM) in hair salons. This is of concern, due to the hazardous effects of these chemicals on the individual's health. Nevertheless, a limited exposure assessment of indoor air quality (IAQ) has been performed. The aim of this study was to analyze the indoor air in 5 hair salons, to assess the IAQ of the occupational exposure, and to identify the main VOCs produced or activities responsible for the respective indoor pollution. The chemical analysis took place inside the salon environment by monitoring the emitted VOCs, as well as the PM of 1, 2.5, 4, and 10 μm aerodynamic diameter. The sampling of VOCs was performed by adsorption of pollutants on Tenax TA sorbent tubes, that were subsequently analyzed using a thermal desorption unit coupled to gas chromatography/mass spectrometry (TD-GC/MS). The obtained results showed that hair products are a major source of air pollutants, as elevated concentrations of VOCs and PM are released in the working environment. Furthermore, the type and concentration of VOCs are affected by the various hair treatment activities taking place in the salons. Among the main compounds detected there was benzene, toluene, ethylbenzene, and xylenes, known as BTEX, as well as, diethyl phthalate, 1,4-dioxane, etc. More than 50 VOCs were identified (occurrence ≥60%) and 14 of them were quantified. Their average concentration levels varied from 12 μg m−3 for naphthalene to 941 μg m−3 for toluene. The measuring levels for PM revealed violations of the EPA and WHO international standards for permissible limit concentrations; this was the case in all hair treatment activities but mostly during keratin treatment. Hence, much more effort is needed to reduce the respective concentration levels of PM and VOCs, that contribute to self-reported health problems.

COVID-19 and Air Pollution: A Spatial Analysis of Particulate Matter Concentration and Pandemic-Associated Mortality in the US.

In 2019, a novel coronavirus, SARS-CoV-2, was first reported in Wuhan, China. The virus causes the disease commonly known as COVID-19, and, since its emergence, it has infected over 252 million individuals globally and taken the lives of over 5 million in the same time span. Primary research on SARS-CoV-2 and COVID-19 focused on understanding the biomolecular composition of the virus. This research has led to the development of multiple vaccines with great efficacy and antiviral treatments for the disease. The development of biomedical interventions has been crucial to combating this pandemic; additionally, environmental confounding variables that could have exacerbated the pandemic need further assessment. In this research study, we conducted a spatial analysis of particulate matter (PM) concentration and its association with COVID-19 mortality in the United States. Results of this study demonstrate a significant positive correlation between PM concentration levels and COVID-19 mortality; however, this does not necessarily imply a causal relationship. These results are consistent with similar studies in Italy and China, where significant COVID-19 cases and corresponding deaths were exhibited. Furthermore, maps of the data demonstrate clustering of COVID-19 mortality which suggest further investigation into the social determinants of health impacting the pandemic.

Air Pollution Associated with Total Suspended Particulate and Particulate Matter in Cement Grinding Plant in Vietnam

Air pollution associated with suspended particles has become a significant concern in Vietnam recently. The study aimed to (1) investigate dust sources; (2) measure concentration levels of Total Suspended Particulate (TSP), Particulate Matter (PM) fractions; (3) identify silica levels and the correlation with respirable particles at a cement grinding plant in Vietnam. A total of 312 samples (52 TSP, 160 PMs) at 13 processes were measured using the direct-reading dust meter. The silica composition was analyzed in a certified laboratory using the X-ray fluorescence (XRF) technique. SPSS version 26 for Window was used to analyze the data. The operations of the cement grinding plant created multiple dust sources from the jetty to the cement dispatch process. The TSP levels ranged 0.06–38.24 mg m−3, and 40.38% (n = 21) TSP samples exceeded the Permissible Exposure Limit (PEL) for an 8-h working shift. Besides that, there was a wide range and significant concentration levels of PMs in the cement processes. The levels of PMs were PM1 (0.00–0.06 mg m−3), PM2.5 (0.01–0.83 mg m−3), PM4 (0.02–4.59 mg m−3), PM7 (0.03–16.94 mg m−3), and PM10 (0.04–26.85 mg m−3). The highest mean levels of PMs factions were measured at the pre-grinding process. The inefficient operation of the dust collector contributed a significant factor to the dust dispersion in this process. The silica’s mean (SD) composition in respirable dust was 20.4% (0.86) and was not significantly different amongst the processes. There was a significant correlation between the levels of respirable dust and silica exposure in the cement grinding plant (r = 0.99). The improvement of indoor air quality is needed to prevent health effects on cement workers.

Identification of sites with elevated PM levels along an urban cycle path using a mobile platform and the analysis of 48 particle bound PAH

Using a multidisciplinary approach, this study provides insight into everyday exposure scenarios of urban commuters to aerosol particles and their associated polycyclic aromatic hydrocarbons. It combines high spatiotemporal resolution particle measurements, used to determine the distribution of particulate matter in the urban environment, with the chemical analysis of the health-relevant particle fraction (< 2.5 μm) at higher polluted sites. In September 2019, multiple test rides were conducted using a mobile platform along an urban cycle path in Aachen, Germany. With this, particle number and mass concentrations were recorded. Based on this data, five sites with elevated levels of particulate matter concentrations were identified. At these locations, the airborne respirable particles (PM2.5) were collected using a high-volume sampler. These were then quantified for an extended set of PAH and alkylated PAH by GC-MS. The alkylated distribution pattern of PAH in the samples all exhibited a typical pyrogenic pattern. A more detailed assessment of toxicity was possible because PAH such as dibenzo[a,l]pyrene or 7H-benzo[c]fluorene could be taken into account. The non-EPA PAH contributed up to 81% to the total toxicity of the particles expressed in toxic equivalents. The measurement of an extended set of PAH was beneficial and is highly recommended for future studies.

A Particulate Matter Concentration Prediction Model Based on Long Short-Term Memory and an Artificial Neural Network.

Many countries are concerned about high particulate matter (PM) concentrations caused by rapid industrial development, which can harm both human health and the environment. To manage PM, the prediction of PM concentrations based on historical data is actively being conducted. Existing technologies for predicting PM mostly assess the model performance for the prediction of existing PM concentrations; however, PM must be forecast in advance, before it becomes highly concentrated and causes damage to the citizens living in the affected regions. Thus, it is necessary to conduct research on an index that can illustrate whether the PM concentration will increase or decrease. We developed a model that can predict whether the PM concentration might increase or decrease after a certain time, specifically for PM (fine PM) generated by anthropogenic volatile organic compounds. An algorithm that can select a model on an hourly basis, based on the long short-term memory (LSTM) and artificial neural network (ANN) models, was developed. The proposed algorithm exhibited a higher F1-score than the LSTM, ANN, or random forest models alone. The model developed in this study could be used to predict future regional PM concentration levels more effectively.

Retraction Note: Ambient particulate matter concentration levels of Ahvaz, Iran, in 2017.

Dust storm in Khuzestan region is strongly influenced by transportation and influx of large amount of particulate matter from internal sources (Hawizeh Marshes and East Ahwaz) and external sources (the Arabian Desert in Saudi Arabia, Jordan, Syria and the Sahara Desert). Particulate matter is one of the main components of indoor and outdoor air quality that can be very dangerous for human. The principal objective of this study was the pinpoint of the source of airborne particulate matter by the NOAA HYSPLIT model in Ahvaz City, southwest of Iran. The investigation of dust storm and their origin was performed by the GFSG Meteorological Data (backward trajectories ending analysis of the NOAA HYSPLIT model) and collecting particulate samples with high-volume air samplers during the fall and winter seasons. The results showed the average ambient particulate matter concentration in the cold and warm seasons was 158 and 161 µg/m3, respectively. Moreover, the average particulate matter concentration in the cold season was significantly higher than the standard level as presented in the National Ambient Air Quality Standard. Consequently, it seems essential to develop green space, decrease particulate emission from source and make determined efforts to control dust at governmental and international scales.

On the effects of COVID-19 safer-at-home policies on social distancing, car crashes and pollution

This paper investigates the impacts of COVID-19 safer-at-home polices on collisions and pollution. We find that statewide safer-at-home policies lead to a 20% reduction in vehicular collisions and that the effect is entirely driven by less severe collisions. For pollution, we find particulate matter concentration levels approximately 1.5 μg/m3 lower during the period of a safer-at-home order, representing a 25% reduction. We document a similar reduction in air pollution following the implementation of similar policies in Europe. We calculate that as of the end of June 2020, the benefits from avoided car collisions in the U.S. were approximately $16 billion while the benefits from reduced air pollution could be as high as $13 billion.